We validated our framework with extensive experiments in the HAN OAR segmentation task of StructSeg 2019 challenge, and a mixed HAN dataset from three sources. With about 1/3 of the parameters of 3D-UNet, our 3D-SepNet achieved prominent improvement in both DSC and 95%HD with the same setting for training, demonstrating the redundancy of usual 3D convolution and that our spatially separable convolution method could deal with anisotropic images more effectively. The experiments found that our 3D-SepNet outperformed 3D-UNet for some flat organs, and it is because 3D-UNet contain more inter-slice convolution operations than our 3D-SepNet and will blur through-plane boundaries. For these flat organs, blurring the through-plane boundaries makes it harder for accurate segmentation. Therefore, using a relatively small number of inter-slice convolution helps to alleviate this problem and obtains better results for organs that are only present in few axial slices. Some other techniques like group-wise [1]} and depth separable convolution [2]} could also be combined with our method to build more lightweight and efficient networks.
